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Biological data visualization is a branch of bioinformatics concerned with the application of computer graphics, scientific visualization, and information visualization to different areas of the life sciences. This includes visualization of sequences, genomes, alignments, phylogenies, macromolecular structures, systems biology, microscopy, and magnetic resonance imaging data. Software tools used for visualizing biological data range from simple, standalone programs to complex, integrated systems.

An emerging trend is the blurring of boundaries between the visualization of 3D structures at atomic resolution, the visualization of larger complexes by cryo-electron microscopy, and the visualization of the location of proteins and complexes within whole cells and tissues.[1][2] There has also been an increase in the availability and importance of time-resolved data from systems biology, electron microscopy, and cell and tissue imaging.[3][4]

Sequence Alignment Visualization[edit]

Sequence data visualization plays a crucial role in molecular biology research, allowing scientists to interpret and analyze complex genetic information in a meaningful way. By visually representing genomic data, researchers can identify patterns, variations, and relationships within sequences, leading to valuable insights and discoveries. Visualization tools such as genome browsers and comparative analysis software enable researchers to explore genetic data in a user-friendly and interactive manner, facilitating the understanding of genetic structures and functions.

A multiple sequence alignment of the WPP domain Source: From Wikimedia Commons, the free media repository. Retrieved from https://commons.wikimedia.org/wiki/File:WPP_domain_alignment.PNG on April 16, 2024

Sequence alignment visualization plays a crucial role in bioinformatics and genomics by enabling researchers to interpret and analyze complex genetic data effectively. Visualizing sequence alignments allows for the identification of similarities, differences, conserved regions, and evolutionary patterns within DNA or protein sequences, aiding in understanding genetic relationships, functional elements, and evolutionary processes. Sequence alignment visualization is essential for several reasons:

Identifying Conserved sequence regions: Visualization helps researchers identify conserved regions across sequences, which are indicative of functional importance or evolutionary relationships [5].

Detecting Mutations and Variations: Visualization tools enable the detection of mutations, insertions, deletions, and other variations within sequences, providing insights into genetic diversity and disease-causing mutations [6].

Understanding Evolutionary Relationships: By visualizing sequence alignments, researchers can infer evolutionary relationships, construct phylogenetic trees, and study the evolutionary history of species or genes [7].

Predicting Functional Elements: Visualization aids in predicting functional elements such as protein domains, motifs, and regulatory regions within sequences, facilitating functional genomics studies [8].

Comparing Genomes: Comparative genomics studies rely on sequence alignment visualization to compare genomes, identify orthologous and paralogous genes, and study genome evolution across species [9].

Non-homologous exon alignment by an iterative method (a), and by a phylogeny-aware method (b).[citation needed]

Techniques

To visualize sequence alignments and their features, researchers often rely on popular bioinformatics software tools such as Clustal Omega, MUSCLE, T-Coffee, and MAFFT. These tools provide interactive platforms for aligning sequences, highlighting conserved regions, displaying sequence variations, and identifying sequence motifs. Additionally, visualization software like Jalview, BioEdit, and Geneious offer advanced features for visualizing and analyzing sequence alignment, making it easier for researchers to interpret and extract meaningful information from genetic data.

Besides software tools, several popular techniques exist for genomic sequence alignment visualization, which plays a crucial role in helping researchers understand genetic relationships, functional elements, and evolutionary processes. Among popular tools, common techniques in sequence alignment visualization include:

A diagram derived from counts of the bases in the translation initiation region from all human genes (en:mRNAs). Each letter is written in proportion to its frequency of occurrence. The letters are stacked together in a en:sequence logo. If a base were used in all ~25,000 genes it would be fully conserved and be drawn two bits tall. The most significantly biased bases are -3 (A) and +4 (G). The en:Initiation codon AUG is not drawn to scale (it would be 2 en:bits) of information on this scale. Source: Wikimedia Commons, the free media repository. Retrieved from https://commons.wikimedia.org/wiki/File:KozakConsensus.jpg on April 16, 2024

Sequence logo: Sequence logos are graphical representations of sequence alignments that display the conservation of residues at each position as well as the relative frequency of each amino acid or nucleotide. Sequence logos provide a compact and informative visualization of conserved sequence and variability [10].

Multiple sequence alignment: Multiple sequence alignment viewers, such as Jalview and MEGA, provide interactive platforms for visualizing and analyzing multiple sequence alignment. These tools offer features for highlighting conserved sequence regions, identifying motifs, and exploring evolutionary relationships within sequences [11].

CYP4F2 protein structure - Protein structure of Leukotriene-B4 omega-hydroxylase 1 enzymeSource: Wikimedia Commons, the free media repository. Retrieved April 16, 2024, from https://commons.wikimedia.org/wiki/File:CYP4F2_protein_structure.png

Protein Structure Alignment Tools are essential instruments in bioinformatics and molecular biology, facilitating the comparison of protein sequences to reveal structural, functional, and evolutionary relationships. Tools like PyMOL and UCSF Chimera employ algorithms to align amino acid sequences, highlighting similarities and differences between proteins, and enabling the visualization of sequence alignments in the context of protein structures. By superimposing aligned sequences onto protein structures, researchers can analyze the spatial arrangement of conserved residues and functional domains [12].

Phylogenetic Tree Visualization: Phylogenetic tree visualization tools, such as FigTree and iTOL, allow researchers to visualize evolutionary relationships inferred from sequence alignments. These tools provide interactive displays of phylogenetic trees, highlighting branch lengths, node support values, and evolutionary distances [13].

Protista taxonomy vs. phylogeny - This diagram shows the phylogeny of eukaryotes based o some recent analyses superimposed over the current kingdom and subkingdom-level taxonomy of protists. The purpose of the image is to demonstrate the paraphyly of most protists groupings, particularly those belonging to kingdom Protozoa: subkingdom Eozoa and subkingdom Sarcomastigota.Source: Wikimedia Commons, the free media repository. Retrieved April 16, 2024 from https://commons.wikimedia.org/wiki/File:Protista_taxonomy_vs_phylogeny.png







Genome browser: Genome browsers like UCSC Genome Browser and Ensembl provide comprehensive platforms for visualizing sequence alignments across entire genomes. Researchers can explore DNA annotation, regulatory elements, and comparative genomics data within the context of genome sequences [14].

View of ENCODE project tracks in the UCSC Genome browser. Source: Wikimedia Commons, the free media repository. Retrieved April 16, 2024 from https://commons.wikimedia.org/wiki/File:EncodeSample.png





Applications

Genomic sequence alignment visualization is used in various applications, playing a crucial role in various areas of genomics and bioinformatics, enabling researchers to analyze, interpret, and extract valuable insights from genetic data. The applications of sequence alignment visualization are diverse and encompass a wide range of research fields. Some key applications include:

Comparative Genomics: Sequence alignment visualization is essential for comparative genomics studies, where researchers compare genetic sequences across different species to identify evolutionary relationships, conserved regions, and functional elements. Visualization tools help in detecting similarities and differences between genomes, aiding in the study of evolutionary processes [15].

Variant Analysis: In the field of genetics and personalized medicine, sequence alignment visualization is used for variant analysis to identify single-nucleotide polymorphism, insertions, deletions, and other genetic variation. Visualization tools help researchers pinpoint specific variations in genomic sequences and assess their potential impact on phenotypic traits [16].

Phylogenetic Analysis: Phylogenetics studies rely on sequence alignment visualization to construct phylogenetic trees and analyze genetic relationships between species or population. Visualization tools enable researchers to visualize sequence similarities, calculate evolutionary distances, and infer phylogenetic relationships based on sequence alignments [17].

Functional genomics: In functional genomics research, sequence alignment visualization is employed to study gene expression, regulatory elements, and protein-protein interactions. By visualizing sequence alignments in the context of functional annotations and gene networks, researchers can elucidate the biological functions and regulatory mechanisms of genes [18].

Structural bioinformatics: Sequence alignment visualization is integral to structural bioinformatics, where researchers analyze protein sequences and structures to understand their three-dimensional organization and functional properties. Visualization tools help in aligning protein sequences, predicting structural motif, and exploring protein-protein interactions [19].

Challenges

Despite the advancements in bioinformatics tools and techniques for visualizing sequence alignments, several challenges persist in this field. These challenges can impact the accuracy and precision, efficiency, and interpretation of sequence alignment data. Some of the key challenges include:

Scalability: As the volume of genomic data continues to increase exponentially, scalability becomes a significant challenge in sequence alignment visualization. Handling large-scale sequence alignments efficiently and effectively, especially in comparative genomics studies, requires robust visualization tools capable of processing massive datasets [20].

Complexity: Genomic sequences are inherently complex, with variations, insertions, deletions, and structural rearrangements that can complicate sequence alignment visualization. Visualizing these complex patterns accurately and intuitively poses a challenge for researchers, particularly when analyzing multiple sequence alignment or genomes simultaneously [21].

Interactivity and User experience: Providing interactive features and a user-friendly interface in sequence alignment visualization tools is crucial for enabling researchers to explore and analyze alignment data effectively. Balancing the complexity of the data with intuitive visualization interfaces presents a challenge in developing user-friendly tools for sequence alignment visualization [22].

Integration of Multiomics Data: With the rise of multi-omics data integration in genomics research, the challenge lies in visualizing and integrating diverse types of data, such as genomics, transcriptomics, proteomics, and epigenomics, in a coherent and informative manner. Developing visualization tools that can effectively integrate and display multi-omics data poses a significant challenge in sequence alignment visualization [23].

Cross-platform Compatibility: Ensuring cross-platform compatibility and accessibility of sequence alignment visualization tools across different operating systems and devices is essential for facilitating collaboration and data sharing among researchers. Addressing compatibility issues and ensuring seamless performance on various platforms present challenges in the development of visualization tools [24].

Future Prospects

Despite challenges, the field of sequence alignment visualization is continuously evolving, driven by advancements in genomics technologies, computational methods, and data analysis techniques. The prospects of sequence alignment visualization hold great promise for enhancing our understanding of genetic data and biological processes. Some key areas of development and research directions include:

Integration of Multiomics Data: The future of sequence alignment visualization involves integrating multi-omics data, including genomics, transcriptomics, proteomics, and epigenomics, to provide a comprehensive view of biological systems. Advanced visualization tools that can effectively integrate and display diverse omics data types will enable researchers to uncover complex relationships and regulatory networks within biological systems [25].

Machine Learning and A.I.: The application of machine learning and artificial intelligence (AI) algorithms in sequence alignment visualization is expected to revolutionize data analysis and interpretation. AI-driven visualization tools can assist in pattern recognition, predictive modeling, and automated annotation of genetic sequences, leading to more efficient and accurate analysis of genomic data [26].

Interactive and Dynamic Visualization: Future developments in sequence alignment visualization will focus on creating interactive and dynamic visualization platforms that allow researchers to explore genetic data in real-time. Interactive tools with user-friendly interfaces and customizable features will enable researchers to manipulate, analyze, and visualize sequence alignments with greater flexibility and control [27].

3D Visualization of Genomic Data: The visualization of genomic data in three-dimensional (3D) space offers a new perspective on understanding the spatial organization and interactions within the genome. 3D visualization tools for sequence alignments can help researchers explore chromatin structure, gene regulation, and genome architecture, providing insights into the functional implications of genetic variations [28].

Cloud-based integration: With the increasing volume of genomic data and the demand for scalable computational resources, cloud-based visualization platforms are expected to play a significant role in sequence alignment visualization. Cloud computing technologies offer on-demand access to high-performance computing resources, enabling researchers to analyze and visualize large-scale genomic datasets efficiently [29].

Macromolecular[edit]

Visualizing macromolecules plays a pivotal role in comprehending the complex structures and functions inherent in biological systems. Advancements in 3D visualization of biological macromolecules, including carbohydrates, proteins, DNA, and RNA, have significantly progressed over the years. Enhanced techniques for visualizing macromolecules have greatly refined our views of complex biological data. They offer clarity and detail that deepen our grasp of how biological entities function and interact.

Techniques

Segmentation enhances biological imaging interpretation, with automated tools improving data analysis. This has led to a rise in web-based visualization for 3D segmentations. Segmentation plays a vital role in deciphering biological imaging data. The advent of sophisticated automated segmentation technologies, along with their incorporation into public imaging data repositories, greatly enhances the interpretation process.[30]

Volume rendering reveals internal macromolecular structures without segmentation, providing a non-invasive view inside the molecules.

Integrating experimental data into visualizations, like overlaying mutations or binding data, offers richer insights. This can be displayed as heat maps or gradients on the molecule, vital for managing the growing complexity of biomolecular data.[31]

Interactive 3D visualization offers hands-on engagement with macromolecules, allowing for manipulation such as rotation and zooming, which enhances comprehension.

Virtual reality and augmented reality present immersive methods to engage with macromolecules, delivering a 3D perspective that screen-based tools can't match. AR app also designed to help students visualize and interact with 3D macromolecular structures, addressing the limitations of traditional 2D images in conveying spatial details and depth perception.[32]

Animation of molecular activities illustrates the dynamic behaviors of biomolecules, serving as a powerful educational and research tool. Utilizing Unity3D game engine technology, this approach democratizes the creation of interactive molecular visualization tools, resulting in a user-friendly platform that simplifies complex biological data depiction.[33]

High-performance computing visualization enables real-time rendering of massive, intricate datasets, a necessity for advanced macromolecular analysis. Software leveraging high-performance computing dynamically and efficiently analyzes drug-receptor interactions via molecular dynamics simulations, offering profound insights and predictions on drug efficacy, and facilitating visualization.[34]

Hybrid visualization techniques merge various methods to provide a multifaceted view of molecules, combining detailed atomic positions with a holistic understanding of structure and volume.

Systems biology[edit]

Systems biology is a branch of biological data visualization dedicated to analyzing and modeling complex biological systems. Popular computational models used in systems biology include process calculi, such as stochastic π-calculus, and constraint-based reconstruction and analysis (COBRA), a paradigm that considers physical, enzymatic, and topological constraints underlying a phenotype in a metabolic network.[35][36]

One of the most popular classes of systems biology is metabolomics. Modern metabolomics imaging uses mass spectrometry to measure metabolite distribution information, then converts the peak intensity associated with the measurement point into an image.[37] Metabolic phenotypes can also be modeled and predicted with genome-scale models using COBRA methods, especially flux balance analysis.[38]

Popular software tools used in systems biology modeling include massPy, Cytosim, and PySB. Further examples may be found at Wikipedia's list of systems biology modeling software.

Magnetic resonance imaging[edit]

Magnetic resonance imaging (MRI) is a common form of biological data visualization used to form pictures of internal biological processes. Different settings of radiofrequency pulses and gradients result in different image appearances; these combinations are known as MRI sequences. A particularly notable subset of MRI is magnetic resonance angiography, which is a group of techniques used to image arteries and veins. MRI's imaging utility is further expanded upon by diffusion MRI and functional MRI, which can be used to capture neuronal tracts and blood flow respectively.

Diffusion MRI further relies on diffusion tensor imaging (DTI), which measures water molecule diffusion and directionality, and diffusion basis spectrum imaging (DBSI), which extracts multiple anisotropic and isotropic diffusion tensors.[39][40] Functional MRI relies on blood-oxygen-level dependent (BOLD) contrast, which measures the proportion of oxygenated hemoglobin in specific areas of the brain; this allows it to measure and model brain activity based on blood flow.[41] Further MRI techniques include saturation pulses (used to reduce motion artifacts), gradient echo (such as dynamic contrast enhancement), spin echo, and diffusion weighting (a signal contrast generation method based on differences in Brownian motion).[42][43][44]

Also of note are computed tomography (CT) and positron emission tomography (PET) scans, which are similar to MRI but rely on different imaging techniques (X-rays and ionizing radiation, respectively). Like MRI, CT scans use numerous methods to display and measure data, including sequential CT (where the CT table steps from location to location), spiral CT (where the entire X-ray tube is spun around the subject), and electron beam tomography (where only the electron paths are spun using deflection coils). PET scanners don’t have quite as much hardware variation and instead use different radiotracers depending on what the imaging target is. These two scanning techniques can also be combined using PET-CT scanners, which are used for the majority of modern PET scans. [45]

Alignment[edit]

A sequence alignment is a way of arranging the sequences of protein, RNA or DNA, to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences. The concept initially compares only two such sequences in the so called pairwise alignment. Global alignments, which attempt to align every residue in every sequence, are most useful when the sequences in the query set are similar and of roughly equal size. Local alignments are more useful for dissimilar sequences that are suspected to contain regions of similarity or similar sequence motifs within their larger sequence context. Multiple sequence alignment is an extension of pairwise alignment to incorporate more than two sequences at a time. Multiple alignment methods try to align all the sequences in each query set. Multiple alignments are often used in identifying conserved sequence regions across a group of sequences hypothesized to be evolutionarily related.

Regular multiple sequence alignment – Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Gaps are inserted between the residues so that identical or similar characters are aligned in successive columns. Many sequence visualization programs also use color to display information about the properties of the individual sequence elements; in DNA and RNA sequences, this equates to assigning each nucleotide its own color. In protein alignments, such as the one in the image above, color is often used to indicate amino acid properties to aid in judging the conservation of a given amino acid substitution. For multiple sequences the last row in each column is often the consensus sequence determined by the alignment; the consensus sequence is also often represented in graphical format with a sequence logo in which the size of each nucleotide or amino acid letter corresponds to its degree of conservation.

Circular multiple sequence alignment – A common assumption of multiple sequence alignment techniques is that the left- and right-most positions of the input sequences are relevant to the alignment. However, the position where a sequence starts or ends can be totally arbitrary. For instance, when linearizing a circular molecular structure, the start of the sequence is selected randomly. This is relevant, for instance, in the process of multiple sequence alignment of mitochondrial DNA, viroid, viral or other genomes, which have a circular molecular structure.

Spiral multiple sequence alignment – The geometry of the spiral sequence alignment is equivalent to a standard linear matrix, with the advantage that it summarizes very long sequences in a practical way.

3D visualization – The 1D-3D Group Alignment Viewer supports exploration of multiple sequence alignments (MSA) at sequence and structure levels for PDB experimental structures and computed structure models (CSMs). It is possible to select proteins and/or residue regions from the MSA to view their 3D structures aligned in Mol*. RCSB.org clusters protein entities (PDB experimental structures and CSMs) by sequence identity threshold and UniProt accession. For each cluster, the MSA is calculated using Clustal Omega and displayed in the 1D-3D Group Alignment Viewer using specific color schemes. PDB protein sequence positions are represented in blue if residue was experimentally determined, and in gray if not. CSMs are colored according to their local pLDDT scores.

Phylogenies[edit]

A phylogenetic tree is a branching diagram or a tree showing the evolutionary relationships among various biological species or other entities based upon similarities and differences in their physical or genetic characteristics. It is a visual representation that shows the evolutionary history between a set of species or taxa during a specific time. Two things are implicitly occurring along the branches of a phylogenetic tree. The first is the passage of time. Deeper nodes are older than the shallower nodes to which the are connected. Thus, deeper nodes indicate both more distant relationships among the terminal taxa that they connect, as well a greater age for the most recent common ancestor of those taxa. The second thing is evolutionary modification, or the accumulation of hereditary genetic and/or structural changes along branches. While these changes are often not shown (mapped) directly on the branches, it is these inferred changes that underpin the construction and interpretation of a phylogenetic tree. When systematists talk about "branch lengths", they are typically referring to the number of these changes. If the "branch lengths" of the tree measure these changes, we also call the tree a phylogram.

Regular phylogenetic tree – Generally called a dendrogram, it is a diagram with straight lines representing a tree.

Cladogram – It is also a diagram with straight lines representing a tree. A cladogram is not, however, an evolutionary tree because it does not show how ancestors are related to descendants, nor does it show how much they have changed, so many differing evolutionary trees can be consistent with the same cladogram.

Circular phylogenetic tree – Circular trees are often used to illustrate relationships among members of major groups of extant organisms, and these trees may have many terminal taxa.

3D Visualization – In a phylogram, we represent the evolutionary distance on one of the axes and the genes on the other. If we would also like to visualize the paralogs, we can add one third axis.

Visualization software[edit]

Name Description Data type Author(s) Year
Cytoscape Open source software platform for visualizing complex biological networks[46] Systems biology Cytoscape Team July 2002
FigTree Java tree viewer able to read multiple tree file formats, color branches, and produce vector artwork Phylogenetic tree Andrew Rambaut Nov 6, 2006
Interactive Tree Of Life (ITOL) Constructs trees and annotates them with various types of data Phylogenetic tree Ciccarelli FD, et al. [47] Mar 3, 2006
Jmol Free, open-source java applet capable of loading multiple molecules with independent movement, surfaces and molecular orbitals, cavity visualization, and crystal symmetry[48] Molecular Dan Gezelter 2001
Medical Image Processing, Analysis, and Visualization (MIPAV) Quantitative analysis and visualization of medical images for modalities such as PET, MRI, CT, or microscopy[49] Medical imaging National Institutes of Health Center for Information Technology Unknown
Medusa Software to build and analyze ensembles of genome-scale metabolic network reconstructions[50] Systems biology Gregory L. Medlock, Thomas J. Moutinho, Jason A. Papin 2001
Molecular Evolutionary Genetics Analysis (MEGA) Provides multiple algorithms to construct phylogenetic trees, including UPGMA, Maximum Likelihood, Maximum Parsimony, etc Phylogenetic tree Masatoshi Nei, Sudhir Kumar, Koichiro Tamura, Glen Stecher, Daniel Peterson, Nicholas Peterson 1993
Molecular Operating Environment (MOE) Models micro- and macromolecules, protein-ligand complexes, and crystal lattices Molecular Chemical Computing Group Unknown
PyMOL Open-source Python application for modeling biological macromolecules Molecular Warren Delano 2017
  • Visual representation of a genome
    Visual representation of a genome
  • ITOL tree of life
    ITOL tree of life
  • Signal transduction chart
    Signal transduction chart
  • Molecular visualization
    Molecular visualization
  • Visualization of a human cell undergoing mitosis
    Visualization of a human cell undergoing mitosis
  • Sagittal fibers pictured using diffusion tensor imaging (DTI)
    Sagittal fibers pictured using diffusion tensor imaging (DTI)

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  50. ^ "Medusa: Software to build and analyze ensembles of genome-scale metabolic network reconstructions". National Library of Medicine. PLoS Comput Biol. April 29, 2020. Retrieved April 16, 2024.

External links[edit]

Related conferences[edit]

source: https://en.wikipedia.org/wiki/Biological_data_visualization

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